Show HN: Trelk – Read, Think, Connect
The article discusses Trelk, a platform that utilizes Google's state-of-the-art embedding model, EmbeddingGemma 300M. This model is designed for on-device deployment and supports over 100 languages, making it a versatile tool for various applications. The model's capabilities include semantic search and clustering, allowing for efficient data processing and analysis.
- ▪Trelk uses Google's EmbeddingGemma 300M model, which has 300M parameters and 768-dimension vectors.
- ▪The model supports multilingual processing for over 100 languages.
- ▪The EmbeddingGemma 300M model is designed for on-device deployment, enabling efficient processing and analysis.
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Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Trelk |
| Canonical URL | https://trelk.app/ |
| Publication time | Fri, 29 May 2026 00:05:39 +0000 |
| Retrieval time | 2026-05-29T00:29:38.907Z |
| Last seen | 2026-05-29T00:29:38.907Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | qNDqeULooJbi |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Embeddings EmbeddingGemma 300M Google's state-of-the-art embedding model built from Gemma 3 and the same research behind Gemini. 300M parameters, 768-dimension vectors, 2K token context. Multilingual support for 100+ languages. Designed specifically for on-device deployment. Semantic search Clustering 768 dimensions On-device
Excerpt limited to ~120 words for fair-use compliance. The full article is at Trelk.
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